Microsoft Fabric Architecture Podcast

By Matthias FallandBusiness Technology

Microsoft Fabric Architecture Podcast episodes

July 31Who Promised That Number?
The Database Hiding Inside Your Scorecard
Episode 31 • 2026-07-31
Duration: 10:32
A scorecard silently creates its own semantic model the moment you hit New. That one architectural fact — that it's a stateful writer, not a read-only lens — explains why RLS doesn't carry over, why refresh breaks upstream, and why rollups leak across permission boundaries.
What we discuss
How it actually works underneath the abstraction
The pattern we keep seeing in the field
Where the obvious answer breaks
A real Reddit/Microsoft Q&A question unpacked
F-SKU realism — what this actually costs
When the rejected approach is actually right
Risks of the recommended path
The architectural principle to take home
The concrete recommended architecture
Key takeaways
Somewhere right now, a scorecard with forty goals is showing all-green because nobody's checked in since March.
So the lesson from all of this — a scorecard's the right tool when you need to know who promised what, and what they said when it slipped.
That's the correct choice for more teams than you'd expect.
Resources
Get started with goals
Deprecation of Metric Sets in Power BI
Fabric Community updates blog
Create scorecards
Scorecard subscriptions
Create subgoals
Hierarchies in scorecards
Create connected goals in Power BI
Stay on top of your goals (check-ins)
Create automated status rules for goals
Protect your scorecards with goal-level permissions
Create linked goals in the Power BI service (preview)
Refresh goals and scorecards automatically
Use Power Automate to update goals automatically
Use Power BI metrics to track goals in Microsoft Teams
About the show
AI-generated voices. Matthias — cloned voice. Fabia — designed AI co-host. See Matthias live on YouTube (Fabric Friday), at his meetups, and at conferences like FabCon.
Hosted by Matthias Falland — Microsoft Data Platform MVP and community architect behind the Fabric Periodic Table. New episodes every Friday.
Submit your case
Have an architecture decision you are wrestling with? DM Matthias on LinkedIn — find him as Matthias Falland. Three to five sentences about the decision, your team size, and your current stack. We anonymize before airing.
This podcast was generated by AI.
Brand design based on fabricperiodictable.com.
9 min
July 24483 Pages and One Design Decision
One Expression Halves Your Data Ceiling
Episode 30 • 2026-07-24
Duration: 10:13
Paginated reports carry no data model, and that one design decision explains every limit teams hit in production. This episode traces the consequences from silent data drops to a tenant-wide security door that only opens one way, and lands on the question that makes the tool choice for you.
What we discuss
How it actually works underneath the abstraction
The pattern we keep seeing in the field
A real Reddit/Microsoft Q&A question unpacked
Where the obvious answer breaks
F-SKU realism — what this actually costs
When the rejected approach is actually right
Risks of the recommended path
The architectural principle to take home
Key takeaways
And a Diagnostics button nobody's pressed is sitting there, ready to explain why.
Somewhere right now, a Monday-morning subscription is carrying a Weekday expression nobody remembers, heading for a ceiling that used to be a slope and is now a cliff.
Show me the query pattern. If the aggregation's in the right place, the report scales. If it isn't, no capacity tier saves you. That's the decision that actually matters.
Resources
What are paginated reports in Power BI?
When to use paginated reports
Paginated reports in the Power BI service
Performance and scalability
FAQ
Paginated Report definition
Access the paginated report editor
SQL Server Reporting Services (Wikipedia)
RDL Schema Specification
Plan to migrate .rdl reports
Supported data sources
Connect via Power Query
Create and use the paginated report visual
Email subscriptions
Export Power BI paginated reports
About the show
AI-generated voices. Matthias — cloned voice. Fabia — designed AI co-host. See Matthias live on YouTube (Fabric Friday), at his meetups, and at conferences like FabCon.
Hosted by Matthias Falland — Microsoft Data Platform MVP and community architect behind the Fabric Periodic Table. New episodes every Friday.
Submit your case
Have an architecture decision you are wrestling with? DM Matthias on LinkedIn — find him as Matthias Falland. Three to five sentences about the decision, your team size, and your current stack. We anonymize before airing.
This podcast was generated by AI.
Brand design based on fabricperiodictable.com.
9 min
July 17Why Can't I Filter My Dashboard?
The Dashboard That Can't Filter
Episode 29 • 2026-07-17
Duration: 9:51
A Power BI dashboard can show tiles from six different semantic models. It cannot filter a single one of them. We explain why the architecture makes that tradeoff inevitable, and what it means for choosing between dashboards and reports.
What we discuss
How it actually works underneath the abstraction
The pattern we keep seeing in the field
A real Reddit/Microsoft Q&A question unpacked
Where the obvious answer breaks
F-SKU realism — what this actually costs
When the rejected approach is actually right
Risks of the recommended path
The concrete recommended architecture
The architectural principle to take home
Key takeaways
Both problems solve the moment you understand what you pinned.
Somewhere right now, someone's pinning a tile and wondering where the slicer went.
Show me the query pattern. If the pattern is 'monitor five numbers from five models,' the dashboard's exactly right. If the pattern is 'explore, filter, drill,' you wanted a report from the start.
Resources
Introduction to dashboards for Power BI designers
Compare Report Server and the Power BI service
Introduction to dashboard tiles
Pin Reporting Services items to Power BI dashboards
Pin an entire report page as a live tile
Reports in Power BI
Create a Real-Time Dashboard
What is Fabric Embed (preview)
Conduct proof of concept to migrate to Power BI
mobile-apps-view-dashboard
service-dashboard-pin-tile-from-excel
service-pin-tile-to-another-dashboard
Dashboards for business users
Create a Power BI dashboard from a report, Considerations and limitations
Create or edit a dashboard tile
About the show
AI-generated voices. Matthias — cloned voice. Fabia — designed AI co-host. See Matthias live on YouTube (Fabric Friday), at his meetups, and at conferences like FabCon.
Hosted by Matthias Falland — Microsoft Data Platform MVP and community architect behind the Fabric Periodic Table. New episodes every Friday.
Submit your case
Have an architecture decision you are wrestling with? DM Matthias on LinkedIn — find him as Matthias Falland. Three to five sentences about the decision, your team size, and your current stack. We anonymize before airing.
This podcast was generated by AI.
Brand design based on fabricperiodictable.com.
10 min
July 10Your Report Isn't Slow
The Bucket Nobody Reads
Episode 28 • 2026-07-10
Duration: 10:31
Every visual on a report page fires a DAX query. When twenty visuals queue behind a parallelism cap, the bottleneck hides in Performance Analyzer's least-read column — and the escape hatch everyone reaches for can silently produce wrong numbers.
What we discuss
How it actually works underneath the abstraction
The pattern we keep seeing in the field
The concrete recommended architecture
Where the obvious answer breaks
A real Reddit/Microsoft Q&A question unpacked
F-SKU realism — what this actually costs
When the rejected approach is actually right
The architectural principle to take home
Key takeaways
Somewhere right now, a report page is loading twenty visuals, queueing twelve of them behind a parallelism cap, and the person watching the spinner is about to open Performance Analyzer, see a DAX number, and start tuning the wrong thing.
Show me the query pattern. That's what this comes down to. A report is a query generator. Every visual is a query, every page load is a fan-out, and every performance problem lives in the gap between what the author designed and what the...
The piece I'd take away is that the report author and the model owner have to be in the same conversation.
Resources
Power BI reports overview
Build Power BI reports with Direct Lake tables
Reports in Power BI - Dashboards versus reports
When to use paginated reports
What are paginated reports
Introduction to dashboards
Git integration source code format
Tour the report editor
Interact with a report in Editing view
Implementation planning: user tools and devices
Direct Lake in Power BI Desktop
Apply data point limits and strategies by visual type
How Direct Lake works
Power BI Desktop project report folder
Connect to semantic models from Power BI Desktop
About the show
AI-generated voices. Matthias — cloned voice. Fabia — designed AI co-host. See Matthias live on YouTube (Fabric Friday), at his meetups, and at conferences like FabCon.
Hosted by Matthias Falland — Microsoft Data Platform MVP and community architect behind the Fabric Periodic Table. New episodes every Friday.
Submit your case
Have an architecture decision you are wrestling with? DM Matthias on LinkedIn — find him as Matthias Falland. Three to five sentences about the decision, your team size, and your current stack. We anonymize before airing.
This podcast was generated by AI.
Brand design based on fabricperiodictable.com.
10 min
July 03When Direct Lake Goes Quiet — Power BI Semantic Models
When Direct Lake Goes Quiet
Episode 27 • 2026-07-03
Duration: 9:40
Direct Lake promises no refresh and VertiPaq speed at lake scale. But its failure modes are silent — it degrades instead of breaking. We pull apart framing, transcoding, and the fallback tax nobody talks about.
What we discuss
How it actually works underneath the abstraction
A real Reddit/Microsoft Q&A question unpacked
Where the obvious answer breaks
The concrete recommended architecture
The pattern we keep seeing in the field
F-SKU realism — what this actually costs
When the rejected approach is actually right
Risks of the recommended path
The architectural principle to take home
Key takeaways
Somewhere right now, a semantic model is serving yesterday's numbers.
Run TABLETRAITS before you ship. One line of DAX. It tells you whether your model is actually in Direct Lake mode — or quietly serving something else.
So the lesson — Direct Lake moved where the discipline lives.
Resources
Power BI semantic models in Microsoft Fabric
Semantic models in the Power BI service
Store data in Microsoft Fabric
Direct Lake overview
Semantic model modes
New name for Power BI datasets
Develop Direct Lake semantic models
Direct Lake in web modeling
How Direct Lake works
Understand Direct Lake query performance
Analyze query processing for Direct Lake semantic models
Cross-workload table maintenance and optimization
Optimize Delta Lake tables with V-Order
Dimensional modeling in Fabric Warehouse
IDEAS journey to a modern data platform
About the show
AI-generated voices. Matthias — cloned voice. Fabia — designed AI co-host. See Matthias live on YouTube (Fabric Friday), at his meetups, and at conferences like FabCon.
Hosted by Matthias Falland — Microsoft Data Platform MVP and community architect behind the Fabric Periodic Table. New episodes every Friday.
Submit your case
Have an architecture decision you are wrestling with? DM Matthias on LinkedIn — find him as Matthias Falland. Three to five sentences about the decision, your team size, and your current stack. We anonymize before airing.
This podcast was generated by AI.
Brand design based on fabricperiodictable.com.
10 min
June 26Why Your Data Agent Gets the Wrong Answer
The Column Named C1
Episode 26 • 2026-06-26
Duration: 9:21
A Fabric Data Agent generates queries from your column names, not your intentions. This episode pulls apart the grounding pipeline to show where accuracy lives and dies — and why the schema investment you skipped three years ago just became urgent.
What we discuss
How it actually works underneath the abstraction
The pattern we keep seeing in the field
Where the obvious answer breaks
A real Reddit/Microsoft Q&A question unpacked
The concrete recommended architecture
F-SKU realism — what this actually costs
When the rejected approach is actually right
Risks of the recommended path
The architectural principle to take home
Key takeaways
Somewhere, a data architect who's been filing those tickets for five years just felt a wave of vindication.
There's something to that. We spent years telling teams to name their columns properly and nobody listened, because the SQL worked either way. Now there's an LLM reading those column names and getting the wrong answer, and suddenly the...
Show me the query pattern — then show me the auth pattern.
Resources
Fabric data agent concepts
data-agent-add-datasources
Use the Fabric data agent in Foundry — Prerequisites
Consume Fabric data agent with Python client SDK
Consume Fabric data agent from Microsoft Foundry Services
Use service principal authentication with Fabric data agent
Evaluate your data agent
Create a Fabric data agent
Best practices for configuring your data agent
Adopt an iterative process to improve your data agent
Semantic model best practices for data agent
Fabric data agent tenant settings
Fabric data agent — Responsible AI FAQ
Data agent configurations
Workspace outbound access protection for Data Agent (Preview)
About the show
AI-generated voices. Matthias — cloned voice. Fabia — designed AI co-host. See Matthias live on YouTube (Fabric Friday), at his meetups, and at conferences like FabCon.
Hosted by Matthias Falland — Microsoft Data Platform MVP and community architect behind the Fabric Periodic Table. New episodes every Friday.
Submit your case
Have an architecture decision you are wrestling with? DM Matthias on LinkedIn — find him as Matthias Falland. Three to five sentences about the decision, your team size, and your current stack. We anonymize before airing.
This podcast was generated by AI.
Brand design based on fabricperiodictable.com.
9 min
June 19The Function That Bills by the Row — Fabric AI Functions
The Function That Bills by the Row
Episode 25 • 2026-06-19
Duration: 9:01
AI Functions let you call GPT from a SELECT statement with no API key. Zero infrastructure — until you check the capacity meter. Two billing streams, no result cache, and a default model eleven days from retirement.
What we discuss
How it actually works underneath the abstraction
Where the obvious answer breaks
A real Reddit/Microsoft Q&A question unpacked
The pattern we keep seeing in the field
F-SKU realism — what this actually costs
When the rejected approach is actually right
The concrete recommended architecture
Risks of the recommended path
The architectural principle to take home
Key takeaways
Ticking right now, in every tenant where someone put ai_classify in a view and moved on.
That quiet meter in your Capacity Metrics app.
Pattern dictates platform. If your pattern is "enrich once, read many," AI Functions are the shortest path in Fabric right now. If that pattern drifts into "enrich on every read," you're billing the LLM like a column default.
Resources
AI Functions overview
Use Azure OpenAI in Fabric with AI Functions (preview)
Warehouse AI functions (preview)
pandas similarity docs
PySpark similarity docs
pandas translate docs
Foundry Tools consumption rate page
Notebooks in Fabric
Fabric Data Warehouse
Apache Spark in Fabric
Copilot in Fabric
Data Agent in Fabric
Lakehouse overview
Customize AI Functions with pandas
Customize AI Functions with PySpark
About the show
AI-generated voices. Matthias — cloned voice. Fabia — designed AI co-host. See Matthias live on YouTube (Fabric Friday), at his meetups, and at conferences like FabCon.
Hosted by Matthias Falland — Microsoft Data Platform MVP and community architect behind the Fabric Periodic Table. New episodes every Friday.
Submit your case
Have an architecture decision you are wrestling with? DM Matthias on LinkedIn — find him as Matthias Falland. Three to five sentences about the decision, your team size, and your current stack. We anonymize before airing.
This podcast was generated by AI.
Brand design based on fabricperiodictable.com.
10 min
June 12The AI That Doesn't Send an Invoice — Copilot in Fabric
The AI That Doesn't Send an Invoice
Episode 24 • 2026-06-12
Duration: 9:11
Copilot in Fabric has no per-user license fee. But every request draws from the same capacity pool as your pipelines and reports. We dig into what 'included' actually costs — and when it throttles production.
What we discuss
How it actually works underneath the abstraction
The pattern we keep seeing in the field
A real Reddit/Microsoft Q&A question unpacked
F-SKU realism — what this actually costs
The concrete recommended architecture
When the rejected approach is actually right
Where the obvious answer breaks
Risks of the recommended path
The architectural principle to take home
Key takeaways
Somewhere right now, there's an F2 running Copilot for a whole team.
The most expensive support ticket you'll ever file is the one where the answer is "rename your columns.
Pattern dictates platform. And with Copilot, the pattern is this — it's a productivity tool that's already wired into your cost structure whether you planned for it or not. The teams that get value from it are the ones who treat enablement...
Resources
copilot-privacy-security
copilot-fabric-overview
data-warehouse/copilot
copilot-enable-power-bi
known-limitations table
Copilot in Fabric consumption
how-copilot-works
Copilot and Agent tenant settings
copilot-notebooks-overview
Enable Fabric Copilot for Power BI
SQL DB Copilot FAQ
overview docs
How Copilot in Microsoft Fabric works
Enable Copilot in Fabric
Fabric Copilot capacity
About the show
AI-generated voices. Matthias — cloned voice. Fabia — designed AI co-host. See Matthias live on YouTube (Fabric Friday), at his meetups, and at conferences like FabCon.
Hosted by Matthias Falland — Microsoft Data Platform MVP and community architect behind the Fabric Periodic Table. New episodes every Friday.
Submit your case
Have an architecture decision you are wrestling with? DM Matthias on LinkedIn — find him as Matthias Falland. Three to five sentences about the decision, your team size, and your current stack. We anonymize before airing.
This podcast was generated by AI.
Brand design based on fabricperiodictable.com.
10 min
June 05When Autolog Stops Logging — Fabric Experiments
When Autolog Stops Logging
Episode 23 • 2026-06-05
Duration: 8:47
Fabric's MLflow autologging promises zero-effort experiment tracking — until you train with LightGBM or XGBoost and discover your metrics column is blank. We pull apart the framework gap, the compounding exclusive flag, and the Git backup that isn't one.
What we discuss
How it actually works underneath the abstraction
Where the obvious answer breaks
A real Reddit/Microsoft Q&A question unpacked
The pattern we keep seeing in the field
Risks of the recommended path
F-SKU realism — what this actually costs
When the rejected approach is actually right
The concrete recommended architecture
The architectural principle to take home
Key takeaways
Somewhere right now, a training run just finished.
And name your runs. Pass run_name to start_run. Forty runs all called Run followed by a number, and the comparison pane becomes useless regardless of how clean the rest of your tracking is.
The takeaway I'd leave anyone starting with Fabric Experiments — the abstraction is real, but it has seams you need to know on day one.
Resources
mlflow-upgrade
Machine learning experiments in Microsoft Fabric
Autologging in Microsoft Fabric
What is Data Science in Microsoft Fabric?
Analyze and train data in Microsoft Fabric
Hyperparameter tuning in Fabric
Perform hyperparameter tuning with FLAML
Training visualizations for AutoML
MLflow 3 in Fabric Data Science
Tutorial: Create, evaluate, and score a churn prediction model
Tutorial Part 3: Train and register a machine learning model
Train models with scikit-learn in Microsoft Fabric
Machine learning experiments and models Git integration and deployment pipelines (Preview)
Data science roles and permissions
Lineage for models and experiments
About the show
AI-generated voices. Matthias — cloned voice. Fabia — designed AI co-host. See Matthias live on YouTube (Fabric Friday), at his meetups, and at conferences like FabCon.
Hosted by Matthias Falland — Microsoft Data Platform MVP and community architect behind the Fabric Periodic Table. New episodes every Friday.
Submit your case
Have an architecture decision you are wrestling with? DM Matthias on LinkedIn — find him as Matthias Falland. Three to five sentences about the decision, your team size, and your current stack. We anonymize before airing.
This podcast was generated by AI.
Brand design based on fabricperiodictable.com.
9 min
May 29Your Endpoint Falls Asleep After Five Minutes — Fabric ML Models
ML Models in Fabric: Training, Deployment, and When to Stay on Azure ML
Episode 22 • 2026-05-29
Microsoft Fabric ships its own MLflow registry — but is it a replacement for Azure Machine Learning? Matthias and Fabia work through the four-layer registry model, PREDICT versus Model Endpoints, the Direct Lake prediction loop, and the architectural question that actually determines the answer: where do your predictions land?
What we discuss
A real-world mistake from a pre-Fabric era
The one question that reframes the architectural debate
How we got here — predecessor products and evolution
Why the "obvious" answer is often wrong
A real Reddit/Microsoft Q&A question unpacked
The concrete recommended architecture
F-SKU realism — what this actually costs
When the rejected approach is actually right
Risks of the recommended path
What Microsoft is shipping that changes the calculus
The architectural principle to take home
Key takeaways
Where do the predictions land. That question answers the architecture. OneLake plus Power BI Direct Lake — Fabric ML Model, genuinely the right call. REST API for an app — evaluate Endpoints maturity or route to Azure ML. GPU training,...
I'd go further. Already on Databricks with Unity Catalog? Don't migrate. Fabric ML Model is not a migration target for Databricks shops — the platform maturity gap is real. The hybrid that actually works: train on Azure ML with GPU,...
For Power BI shops — yes. PREDICT writes predictions to a Delta table in OneLake, Direct Lake reads it with zero copy, zero scheduled refresh. That eliminates an entire class of ETL work. But only if Power BI is your audience.
Resources
ML Experiment
Notebooks
Lakehouse
Direct Lake
Code-first AutoML
Low-code AutoML
SynapseML
Activator
Machine learning model in Microsoft Fabric
What is Data Science in Microsoft Fabric?
Tutorial Part 3: Train and register a machine learning model
Tutorial Part 4: Perform batch scoring and save predictions
Machine learning model scoring with PREDICT
Serve real-time predictions with ML model endpoints (Preview)
Train models with scikit-learn in Microsoft Fabric
About the show
Built on ElevenLabs voice synthesis. Matthias — cloned voice. Fabia — designed AI co-host. See Matthias live on YouTube (Fabric Friday), at his meetups, and at conferences like FabCon.
Hosted by Matthias Falland — Microsoft Data Platform MVP and community architect behind the Fabric Periodic Table. New episodes every Friday.
Submit your case
Have an architecture decision you are wrestling with? DM Matthias on LinkedIn — find him as Matthias Falland. Three to five sentences about the decision, your team size, and your current stack. We anonymize before airing.
Built on ElevenLabs voice synthesis. Brand design based on fabricperiodictable.com.
9 min

About Microsoft Fabric Architecture Podcast

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Architecture decisions for Microsoft Fabric. Anonymized real customer scenarios, cost realism, counter-arguments included. Weekly episodes aligned with Fabric Friday recordings.

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Microsoft Fabric Architecture Podcast

Microsoft Fabric Architecture Podcast episodes

About Microsoft Fabric Architecture Podcast